Identification and classification of actions is of interest in machine learning applications as it provides a mechanism for training robots of the consequences of different actions. For example, data describing causal relationships can be used to specify how different actions affect different objects. Such relational data can be used to generate a “commonsense” database for robots describing how to interact with various types of objects.
However, conventional techniques for acquiring cause-effect relationships are limited. Existing distributed collection techniques receive relationship data from volunteers, which provides an initial burst of data collection. However, over time, data collection decreases to a significantly lower amount. Hence, conventional data collection methods are not scalable to provide a continuous stream of data.
What is needed is a system and method for automatically extracting causal relations from gathered text.